基于神经网络逆控制的水轮机调节系统

根据神经网络对非线性系统模型的辨识能力,将其与自适应逆控制相结合,对水轮发电机组的逆模型进行建模,构建一种新的水轮机调节系统。该方案以逆系统以及系统辨识理论为基础,以水轮发电机组作为被控对象,分别针对其频率和负荷扰动,建立神经网络在线逆控制器,对系统进行调控,并将仿真结果与传统PID控制进行比较。从仿真结果可以看出,所提的控制方案能够实现对水轮发电机组的有效控制,使系统具有较好的动态性能和鲁棒性。     

基于RBF-PID的水轮发电机组控制系统

研究水轮发电机组稳定性控制优化问题,水轮发电机组是一个非线性、时变的复杂控制系统,很难建立精确模型。采用常规PID控制策略难以较高的控制精度,超调量大。为提高水轮发电机组控制精度,将自学习较强的RBF神经网络与常规PID相结合,提出一种基于RBF-PID组合的水轮发电机组控制算法。采用RBF神经网络对水轮发电机组控制系统的Jacobian矩阵信息进行在线辨识,实现RBF-PID参数在线自整定。仿真结果表明:RBF-PID组合控制器不仅提高控制系统的精度,而且超调量小、抗扰动能力强,能够很好实现水轮发电机组的稳定性优化控制。     

基于改进混沌优化算法的综合负荷模型参数辨识

参数在线辨识是目前电力系统负荷建模的主要手段,而在辨识方法上主要使用了优化类算法.混沌优化算法是一种新型搜索算法,目前在电力系统负荷预测、无功优化中已有应用.该算法改进了以往混沌优化算法的流程,增加了参数搜索范围自动缩小的功能,减少了一次混沌序列生成的步骤.对测试函数的优化结果表明,改进算法在保证精度的基础上大大提高了寻优速度.将该改进算法应用到了直接考虑配电网的综合负荷模型的参数辨识上,仿真结果说明该算法寻优速度快,并且有良好的辨识精度.通过对仿真结果的分析指出,对于负荷模型参数辨识,混沌序列的迭代次数不必超过五万次,合理缩小参数寻优范围有助于提高算法的精度.     

l1鲁棒辨识:一种递推插值方法

针对l1鲁棒辨识不能有效利用试验数据和进行在线辨识的问题,提出了一种在线递推插值辨识方法.用几何方法描述试验信息,利用系统可行集与新的试验信息所构成的半空间的包含关系判断数据信息,有效地利用了试验数据,提高了辨识精度.同时提出了一种新的计算辨识误差紧界的方法.仿真结果表明了算法的有效性和可行性.     

基于PMU的次/超同步谐波参数精确估计方法

随着可再生能源并网发电和远距离交直流输电等应用的快速发展,电力电子技术被广泛应用于电力系统领域。大量传统电力设备被电力电子设备取代,使得电网谐波干扰、次同步振荡等扰动现象更加频繁。为了及时监测电力系统中因次同步振荡引起的次同步和超同步振荡谐波分量,采取措施保障机组安全和电力系统稳定运行,对遵循IEEE Std C37.118-2011标准的同步相量测量单元(PMU)装置测得的同步相量数据应用于主站监测次同步和超同步谐波分量是时存在的问题进行了分析,指出了其局限性。结合快速傅里叶变换(FFT)频谱修正理论,提出了在PMU装置上实现分布式次同步和超同步谐波分量参数估计的高精度辨识方法。总结了其中的关键技术,并通过仿真试验验证了该方法的可行性和准确性。该方法在PMU装置上实现了分布式的次同步和超同步振荡参数在线辨识精确估算。通过试验和实际工程数据,验证了PMU实现次同步和超同步振荡在线辨识告警的可行性和准确性。     

汽轮发电机组在线监测与故障诊断系统

200 MW及以上容量的大型发电机组的安全稳定运行是电力系统安全运行的保证,为了保障汽轮发电机组的安全可靠运行,必须实现机组运行信息内部共享和远程监测和故障诊断。因此,提出了一种基于Internet网络和CAN总线的分布式汽轮发电机组在线监测与故障诊断系统;给出了分布式在线监测和故障诊断系统的硬件结构、软件配置和功能;介绍了诊断系统的网络技术和实现框架,应用实例证明该系统具有一定的实用性和有效性。     

l1鲁棒辨识:一种递推插值方法

针对l¹鲁棒辨识不能有效利用试验数据和进行在线辨识的问题, 提出了一种在线递推插值辨识方法. 用几何方法描述试验信息, 利用系统可行集与新的试验信息所构成的半空间的包含关系判断数据信息, 有效地利用了试验数据, 提高了辨识精度. 同时提出了一种新的计算辨识误差紧界的方法. 仿真结果表明了算法的有效性和可行性.     

基于神经网络和遗传算法的智能PID控制

提出了一种结合神经网络和遗传算法的智能PID控制算法;该控制器先利用RBF辨识网络在线辨识系统模型,再利用遗传算法在线调整PID三个控制参数,将传统的Ziegler-Nichols方法所得的控制参数作为遗传算法的初始参数范围,缩小了遗传算法的寻优范围;在MATLAB6.5环境下进行仿真和试验研究,结果证明RBF辨识网络的输出能够很好地跟踪对象输出,遗传算法很好地优化了控制参数;二者结合可在线有效地控制较复杂的被控对象.     

水轮机传递参数在线辨识

针对水轮机的六参数模型,本文利用最小二乘法的思想对水轮机模型参数进行辨识,推导了参数在线辨识算法。通过仿真,得到了较好的辨识结果。在此辨识结果的基础上,可以在线整定控制器的控制参数,实现自适应控制。     

系统辨识、状态估计和不变性原理在电力系统负荷频率控制中的应用

本文从随机控制理论角度研究电力系统的负荷频率控制问题。首先,应用辨识方法建立负荷扰动模型。其次,按分层估计原理构造各级状态估计器。最后,应用不变性原理导出负荷频率控制律。对于包含一台汽轮发电机组和一台水轮发电机组的简单系统所进行的模拟表明,按上述原理设计的负荷频率控制器比目前采用的积分式反馈控制器更易于适应各种负荷扰动,控制性能更好。     

Fault-tolerant cruise control of electric vehicles with induction motors

A fault-tolerant control scheme is proposed for the cruise control of electric vehicles (trains, cars) that make use of induction motors. It relies on a rotor speed reference generator and on a flux observer which is adaptive with respect to the uncertain rotor and stator resistances and to the load torque as well. The closed loop on-line identification of those three critical uncertain parameters allows for: (i) on-line estimating and imposing the motor flux modulus reference value which minimizes power losses at steady-state and improves power efficiency; (ii) the on-line detection of speed sensor faults as well as the fast switching on redundant motor speed sensors. CarSim simulations illustrate the effectiveness of the proposed approach.     

A study of frequency prediction for power systems

A frequency predictor is identified from simulated measurements of power and frequency on a power system. An on-line Ieast-squares algorithm is used along with a new system structure test for model order identification. A comparison of this system structure test with other model order identification tests is also included. The performance of the resultant predictor is then determined as a function of both the prediction interval and the sampling rate and measurement noise levels on the power and frequency measurements used for the predictor. The results indicate an increase in prediction error with the length of the prediction interval because the predictor loses its principal dependence of "P-f" (power-frequency) dynamics in the power system and depends more strongly on the random load fluctuations over the prediction interval. The modeling error was shown to be unaffected by sampling rate and by measurement noise levels below that of the present power-frequency recorder [2], but was affected by measurement noise levels above the values on the present recorder. This accuracy of the model for small prediction intervals justifies the future use of frequency measurements in power system identification and justifies the use of least-squares algorithms using these measurements. The results on sampling rate and measurement noise imply that the present recorder [2] is an "optimal" design and that the RTDAS [5] will be an even better tool for use in power system model identification.     

发电机调速系统的模糊逻辑控制器

本文基于发电机速度／加速度相平面上的标准模糊隶属函数提出了电力系统发电机调速系统的模糊逻辑控制器。这种模糊逻辑控制器的输入信号可由模糊隶属函数和增益所确定，其中模糊控制器的增益可以表示为系统短路功率的函数，由此，整个控制器不需任何参数调整。通过对简单电力系统的仿真研究表明，调速系统的模糊逻辑控制器对于系统参数和运行方式的变化及不同的故障程度等都具有很强的鲁棒性和很高的智能水平。     

一种新型的摩托车磁电机在线微机检测系统的开发与研究

针对目前我国磁电机产品在线检测设备的落后现状,采用现代微机检测与控制技术开发了一种新型的摩托车磁电机在线微机检测系统;该系统采用独特的设计方法,提高了检测对象的装夹速度和灵活方便性,采用特别的信号处理和算法显著提高了摩托车磁电机点火提前角的数字化检测精度;应用结果表明,该检测系统运行稳定可靠,检测精度高,具有一定的实用性和推广价值.     

Performance investigation of a permanent magnet generator

Computer simulation using the finite-element method (FEM) is an important tool for the design of highly efficient power devices. In this work combination of FEM-software for magnetic analysis and Simulink-software for non-linear parameter identification for dynamics of a permanent magnet (PM) generator is discussed. An FE model of the generator is developed and its electromagnetic torque analysis is carried out using FEM-software. Simulink has been used for analysis of rotor moment of inertia (MI) due to variation of winding resistance and winding inductance. It is shown that system MI has a significant effect on optimal winding resistance and inductance to achieve steady state operation in shortest period of time.     

飞机发电机测试设备中的软件设计

发电机是飞机的关键动力设备,是飞机上精密电子装置的主要供电系统。检查它的工作状态对于确保飞机安全运行,有着十分重要的指导意义。本文介绍了一个用于民用航空飞机发电机检测的测试系统,分析了其系统性能和工作原理,阐述整个系统的软件设计思想和软件功能。     

NSG水位神经自适应PID控制与仿真研究

核动力蒸汽发生器(NSG)是一个高度复杂的非线性时变系统。由于蒸汽发生器在瞬态、启动和低功率下的“收缩”与“膨胀”现象引起的逆动力学效应,使蒸汽发生器的水位控制变得复杂。本文针对传统的核动力蒸汽发生器水位PID控制方法存在的缺点,将神经网络方法与PID控制的结构结合起来,提出了核动力蒸汽发生器水位神经自适应PID控制方法。采用BP学习算法调整控制器神经网络的连接权值,实现了控制器参数的在线整定。仿真研究表明,所设计的控制器具有良好的控制性能,且结构简单,易于实现。     

船舶动力与传动装置润滑油状态监测

针对目前舰船动力系统故障定位难、预判不准确的现状,提出了一种应用于船舶动力与传动装置的油液在线监测技术。通过分析舰船动力系统的故障源,阐述了船舶动力与传动装置滑油状态监测的内容,包含油液黏度检测、油液水分检测与油液金属磨粒检测。结合黏度、水分与磨粒检测原理,设计了滑油状态监测试验。通过实船试验,验证了油液在线监测技术在船舶动力与传动装置状态监测中应用的可行性和有效性。     

Trajectory generation and control of a knee exoskeleton based on dynamic movement primitives for sit-to-stand assistance

This paper presents a novel control approach for a knee exoskeleton to assist individuals with lower extremity weakness during sit-to-stand motion. The proposed method consists of a trajectory generator and an impedance controller. The trajectory generator uses a library of sample trajectories as the training data and the initial joint angles as the input to predict the user’s intended sit-to-stand trajectory. Utilizing the dynamic movement primitives theory, the trajectory generator represents the predicted trajectory in a time-normalized and rather a flexible framework. The impedance controller is then employed to provide assistance by guiding the knee joint to move along the predicted trajectory. Moreover, the human-exoskeleton interaction force is used as the feedback for on-line adaptation of the trajectory speed. The proposed control strategy was tested on a healthy adult who wore the knee exoskeleton on his leg. The subject was asked to perform a number of sit-to-stand movements from different sitting positions. Next, the measured data and the inverse dynamic model of the human-exoskeleton system are used to calculate the knee power and torque profiles. The results reveal that average muscle activity decreases when the subject is assisted by the exoskeleton.     

A computationally efficient approach for NN based system identification of a rotary wing UAV

Neural Network (NN) models based on autoregressive structures have long been used for nonlinear system identification problems. Their application for on-line implementations, however require them to be trained within a prescribed time span, which is often related to the sampling time of the system. In this paper, we introduce a NN model that is embedded with a dimensionality reduction mechanism in order to reduce the size of the network. The dimensionality reduction is based on Principal Component Analysis (PCA) and the resulting smaller NN trains faster. The longitudinal and lateral dynamics of a rotary wing Unmanned Aerial Vehicle (UAV) is modelled using flight test data. The results of system identification, error statistics and training times are provided to highlight the benefits of the proposed approach for NN based system identification models.